IEEE Trans Pattern Anal Mach Intell. 2018 Jun;40(6):1494-1501. doi: 10.1109/TPAMI.2017.2716350. Epub 2017 Jun 16.
Matching-based algorithms have been commonly used in planar object tracking. They often model a planar object as a set of keypoints, and then find correspondences between keypoint sets via descriptor matching. In previous work, unary constraints on appearances or locations are usually used to guide the matching. However, these approaches rarely utilize structure information of the object, and are thus suffering from various perturbation factors. In this paper, we proposed a graph-based tracker, named Gracker, which is able to fully explore the structure information of the object to enhance tracking performance. We model a planar object as a graph, instead of a simple collection of keypoints, to represent its structure. Then, we reformulate tracking as a sequential graph matching process, which establishes keypoint correspondence in a geometric graph matching manner. For evaluation, we compare the proposed Gracker with state-of-the-art planar object trackers on three benchmark datasets: two public ones and a newly collected one. Experimental results show that Gracker achieves robust tracking results against various environmental variations, and outperforms other algorithms in general on the datasets.
基于匹配的算法已被广泛应用于平面目标跟踪。它们通常将平面目标建模为一组关键点，然后通过描述符匹配来寻找关键点集之间的对应关系。在以前的工作中，通常使用关于外观或位置的一元约束来指导匹配。然而，这些方法很少利用目标的结构信息，因此受到各种干扰因素的影响。在本文中，我们提出了一种基于图的跟踪器，称为 Gracker，它能够充分挖掘目标的结构信息，从而提高跟踪性能。我们将平面目标建模为一个图，而不是一个简单的关键点集合，以表示其结构。然后，我们将跟踪重新表述为一个连续的图匹配过程，以几何图匹配的方式建立关键点对应关系。为了评估，我们将所提出的 Gracker 与三个基准数据集上的最先进的平面目标跟踪器进行了比较：两个公共数据集和一个新收集的数据集。实验结果表明，Gracker 能够在各种环境变化下实现稳健的跟踪结果，并且在一般情况下优于其他算法。
